1. Technical Field
Embodiments of the present invention relate generally to echo cancellation and, more particularly, to acoustic echo cancellation using an adaptive filter.
2. Description of Related Art
Echo cancellation is an example of signal filtering in which selected, undesired components of a signal are removed. In echo cancellation, echo is removed from an acoustic, echo-contaminated signal in order to improve signal quality. Generally, echo cancellers resolve an estimate of the echo in an echo-contaminated signal and then subtract that estimate from the signal. A known way to make this estimate is with a signal filtering algorithm.
Echo cancellation is an example of adaptive signal filtering and echo cancellers are adaptive filters. Adaptive filters apply algorithms with adjustable coefficients that change in response to changing signal conditions. Adaptive filters, as their name implies, are capable of learning which components of a signal are unwanted, during normal operation and without training. This ability to change offers autonomous adaptability (i.e., the ability to automatically adjust to an unknown environment), which tends to yield more accurate estimates of an echo in applications in which a necessary frequency response may not be known beforehand or may vary with time. Examples of such applications include telecommunications, control systems, and radar systems.
There are known challenges in acoustic echo cancellation, most of which may be categorized as relating to the time variability of signals and the echo signal path. In many applications, the reference and local echo-contaminated signals have natural variations, and a speaker on either side of a conversation, for example, may start and stop talking at any time. These factors cause the statistical properties of the reference and local signals to change quickly and to have a high dynamic range. This is why assumptions that the involved signals are stationary or even slowly changing lead to inferior performance.
In addition, sampling rates can pose a challenge. For example, it is known that in order to get reliable frequency or impulse response estimates in the presence of noise, an average of a significant number of modified estimates of the spectral density of a signal (periodograms) is required. And, an insufficient number of estimates in a given interval increases the likelihood that even significant signal variations may not be registered. This results in echo canceller performance that is effectively determined by the average echo-to-noise ratio (ENR) of the echo-contaminated signal, which is usually significantly lower than its peak.
Approaches to resolve these known problems include using unique methods to detect double talk and changes in the acoustic path, or to control the adaptation gain. These approaches have not been entirely successful, however.